F.3 fMRI-based deep brain stimulation programming: a blinded, crossover clinical trial
Bibliographic record
Abstract
Background: Deep brain stimulation (DBS) in Parkinson’s disease (PD) requires extensive trial-and-error programming, often taking over a year to optimize. An objective, rapid biomarker of stimulation success is needed. Our team developed a functional magnetic resonance imaging (fMRI)-based algorithm to identify optimal DBS settings. This study prospectively compared fMRI-guided programming with standard-of-care (SoC) clinical programming in a double-blind, crossover, non-inferiority trial. Methods: Twenty-two PD-DBS patients were prospectively enrolled for fMRI using a 30-sec DBS-ON/OFF cycling paradigm. Optimal settings were identified using our published classification algorithm. Subjects then underwent >1 year of SoC programming. Clinical improvement was assessed under SoC and fMRI-determined stimulation conditions. Results: fMRI optimization significantly reduced the time required to determine optimal settings (1.6 vs. 5.6 months, p<0.001). Unified Parkinson’s Disease Rating Scale (UPDRSIII) improved comparably with both approaches (23.8 vs. 23.6, p=0.9). Non-inferiority was demonstrated within a predefined margin of 5 points (p=0.0018). SoC led to greater tremor improvement (p=0.019), while fMRI showed greater bradykinesia improvement (p=0.040). Conclusions: This is the first prospective evaluation of an algorithm able to suggest stimulation parameters solely from the fMRI response to stimulation. It suggests that fMRI-based programming may achieve equivalent outcomes in less time than SoC, reducing patient burden while potentially enhancing bradykinesia response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".